Computer Science > Distributed, Parallel, and Cluster Computing

Abstract: In this paper, we explore optimizations to run Recurrent Neural Network (RNN)
models locally on mobile devices. RNN models are widely used for Natural
Language Processing, Machine Translation, and other tasks. However, existing
mobile applications that use RNN models do so on the cloud. To address privacy
and efficiency concerns, we show how RNN models can be run locally on mobile
devices. Existing work on porting deep learning models to mobile devices focus
on Convolution Neural Networks (CNNs) and cannot be applied directly to RNN
models. In response, we present MobiRNN, a mobile-specific optimization
framework that implements GPU offloading specifically for mobile GPUs.
Evaluations using an RNN model for activity recognition shows that MobiRNN does
significantly decrease the latency of running RNN models on phones.

Comments:

Published at 1st International Workshop on Embedded and Mobile Deep Learning colocated with MobiSys 2017